1627results about "Web data querying" patented technology

The invention provides an implementation method, system and equipment of a commercial intelligent system based on a large model ReAct, and belongs to the technical field of artificial intelligence. The method is applied to a ChatBI platform. Comprising the steps that a main agent receives query request information from a user terminal and guides an LLM to conduct cyclic reasoning on the query request information according to a preset ReAct normal form through a preset Prompt project, and the cyclic reasoning process comprises the steps that 1, input information from an upstream task is analyzed through the LLM, and a to-be-executed action corresponding to a downstream task is determined; step 2, according to each action task corresponding to the action to be executed and the action task type thereof, executing a reasoning action; 3, receiving an inference action feedback result, updating a loop inference context, and determining whether to trigger a next round of loop inference or not until a preset loop termination condition is met; and based on each decision analysis result obtained by loop reasoning, generating a query result and sending the query result to the user terminal.

The invention relates to an industrial equipment maintenance intelligent question-answering system based on multi-agent collaboration. Wherein the input unit is used for receiving text, voice, image or equipment scanning and other multi-mode user input information and analyzing the information into structured problem information; the scheduling unit performs semantic understanding and problem classification on the structured problem information based on the fine-tuned cross-language pre-training language model and an incremental training mechanism; the processing unit calls a corresponding domain agent according to the classification result, and generates an intelligent question and answer processing result including predictive maintenance suggestions, structured reply content and semantic annotation information; and the fusion unit fuses the local knowledge base, the graph database and the networking retrieval information, performs multi-hop semantic reasoning on the intelligent question and answer processing result, and generates multi-modal reply information including text description, image screenshots, prediction curves and recommendation links. The system can support multi-language and multi-mode intelligent question answering and predictive maintenance in a complex industrial maintenance scene.

The invention discloses a science and technology fast message intelligence perception system based on a large language model, and belongs to the field of intelligence perception, the system comprises an information collection module, a data standardization module and a database, the information collection module obtains multi-mode science and technology fast message data; the data standardization module is used for receiving the multi-modal data output by the information collection module and executing data cleaning, structured processing and semantic standardization operation; after standardization processing, storing the data into a database; the system further comprises an intelligence value screening module, an evaluation system module and a visualization module. And the intelligence value screening module is combined with the evaluation system to identify the intelligence value, and multi-dimensional visual presentation is realized through the visualization module. The method improves the efficiency and accuracy of information recognition and perception, and has significant advantages in the aspects of processing mass information and enhancing the information perception ability.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

The invention provides a text search method and system based on vector retrieval and large model optimization, and relates to the technical field of text search, and the method comprises the following steps: obtaining a to-be-retrieved text and generating a semantic vector; retrieving similar texts in a vector database based on the semantic vectors; calculating the correlation of the text pairs by using a cross encoder model and sorting the text pairs; and extracting key entities by adopting a knowledge graph optimization model, analyzing an entity relationship, and reordering and filtering search results. According to the method, through semantic vector retrieval, cross coding calculation of correlation and knowledge graph optimization, the accuracy and correlation of text search are improved, user intentions can be better understood, and more accurate search results can be returned.

The invention discloses a public opinion risk dynamic early-warning method and system based on a large language model, and the method and system carry out the semantic recognition, event extraction and situational reasoning of unstructured public opinion data through introducing an advanced large language model, and carry out the dynamic early-warning of the public opinion risk in combination with structured emotion indexes and market dynamics. Potential risk factors in social public opinions can be automatically identified, and risk quantitative evaluation is realized in combination with semantic tags, market behavior data and emotion intensity. The system has strong prospective perception ability and event evolution reasoning ability, can provide rapid and accurate early warning information, and further can generate personalized suggestions and decisions in combination with user risk preferences. The method can be widely applied to a plurality of public opinion sensitive fields such as enterprise brand management, government supervision, financial institutions and media transmission control, and the automation and precision capability of dealing with sudden public opinion risks is effectively improved.

The present application relates to the technical field of electronics, and provides a method for generating a commodity label on the basis of a large language model, and an electronic device. The method specifically comprises: acquiring commodity description information related to a target commodity; performing structured processing on the commodity description information to obtain an objective property feature of the target commodity; and using a fine-tuned large language model to process the objective property feature on the basis of a language prompt lexicon to obtain one or more commodity labels of the target commodity. According to the method, a commodity selling point label can be generated by means of the commodity description information acquired from multiple sources and multiple paths, the source of the commodity description information is expanded, commodity information of different sources can be identified and acquired by means of image and text details, etc., which can assist the model in better understanding the commodity, and generating a more reliable selling point label having a higher confidence level and greater traceability.

The invention belongs to the field of combination of a large model and a knowledge graph, and particularly relates to a large model and knowledge graph combined reasoning method for false news detection, which breaks through the limitation of a traditional detection method in the aspects of knowledge instantaneity and reasoning controllability through heterogeneous architecture design. Based on multi-dimensional features such as timeliness, propagation mode and information density of news propagation, a double-engine-driven intelligent reasoning system is constructed, on one hand, important entity extraction and semantic understanding are carried out through a large language model, and on the other hand, structured fact verification is provided through a dynamic knowledge graph. And the inherent factual illusion problem of a large language model is effectively solved. Compared with a traditional knowledge iteration mode depending on single model parameter updating, the method supports an incremental updating strategy based on knowledge graph nodes.

Aspects of the disclosed technology include computer-implemented systems and methods for moderating generative content produced by machine-learned generative models and providing outputs indicative of query and response validity for a target domain. The system can generate a query association based on a user query and domain artifacts of a target domain and generate a response association based on generative content and the domain artifacts. The system can embed the domain artifacts in a vector embedding space and / or train a machine-learned model using the domain artifacts. The system can determine the validity of queries and responses for the target domain using the vector embeddings and the machine-learned model. The system can moderate content, such as by filtering queries and / or generative content that does not satisfy association criteria for a target domain. The system can generate outputs such as visualizations of query and response distance relative to a target domain.

The invention discloses a data acquisition method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. The data acquisition method is applied to the data acquisition system, and comprises the following steps: acquiring a user query instruction through terminal equipment, and sending the user query instruction to a vehicle based on a cloud server; the method comprises the following steps: acquiring charging information, riding related information and vehicle position information through a vehicle based on a user query instruction; target display information is determined through a vehicle according to charging information and riding related information, and the target display information and vehicle position information are fed back to a cloud server; and sending the target display information and the vehicle position information to the terminal equipment through the cloud server, so that the terminal equipment displays the target display information and the vehicle position information. According to the embodiment of the invention, remote monitoring of the terminal equipment on the vehicle is realized, the target display information and the vehicle position information are acquired in time, possible faults of the vehicle can be found in time, and the use experience of a user is improved.

An offline collection system comprises a pipeline for storing metadata of user interface (UI) elements at web pages of a web application. The pipeline comprises crawling uniform resource locators (URLs) of web pages of the web application for content and rendering screenshots of the web pages. The pipeline then prompts a multimodal large language model (LLM) to generate database entries for the web pages comprising UI element metadata derived from the crawled content and rendered screenshots. A response system receives user queries to navigate the web application and augments prompts to an LLM to respond to the user queries with metadata of UI elements relevant to the user queries stored by the offline collection system.

The present disclosure is related to framework for automatically and efficiently finding machine learning (ML) architectures that are optimized to one or more specified performance metrics and / or hardware platforms. This framework provides ML architectures that are applicable to specified ML domains and are optimized for specified hardware platforms in significantly less time than could be done manually and in less time than existing ML model searching techniques. Furthermore, a user interface is provided that allows a user to search for different ML architectures based on modified search parameters, such as different hardware platform aspects and / or performance metrics. Other embodiments may be described and / or claimed.

An online system performs an inference task in conjunction with the model serving system and / or interface system to generate relevant product images for query auto-completion and query suggestion to help users better navigate their search experience. The online system generates a collection of query suggestions using search query log mining. For each query suggestion in the collection of query suggestions, the online system retrieves one or more catalog images that depict the query suggestion from a product catalog. The online system constructs a prompt to a text-to-image model including the query suggestion, and a request to generate one or more query images based on the query suggestion. The online system receives the query images from the text-to-image model and ranks the catalog and query images to identify an image to display to the user in association with the query suggestion.

The invention discloses an intelligent government affair question counting method and device based on a large model, equipment and a medium, and relates to the field of natural language processing, and the method comprises the steps: carrying out the standardization processing of government affair item consultation questions obtained from a target user side, obtaining corresponding standardization questions, determining a structured query language based on the standardization questions and a preset large model, and obtaining the structured query language; querying in a preset government affair database by using a structured query language; if the corresponding first target government affair data is queried in the preset government affair database, determining a first target result returned to the target user side based on the preset large model and the first target government affair data; and if the first target government affair data is not queried in the preset government affair database, querying in the Internet by using the standardized problem, and determining a second target result returned to the target user side based on the queried second target government affair data and a preset large model. Therefore, the accuracy of the government affair question service can be improved.

The embodiment of the invention provides a cross-modal data search method and device based on Surreal DB. The method comprises the steps that multi-modal data input by a user at a data query interface is received, and the multi-modal data comprises text, picture, audio, video, webpage and document data; the method comprises the following steps: converting audio, video, webpage and document data into texts and pictures, performing keyword extraction on the texts to obtain text cues, and performing vectorization processing on the texts and the pictures to obtain query vectors; performing full-text retrieval on the text cue word based on a Surreal DB database to obtain a text search result, and performing vector retrieval on the query vector to obtain a vector search result; reordering the text search result and the vector search result by using a ranking-based search result fusion algorithm, and outputting a preliminary search result; and performing associated query on the preliminary retrieval result to obtain associated data.

The invention discloses an enterprise exhaustion report intelligent generation method and system based on a large model, and relates to the technical field of artificial intelligence application. The demand understanding module analyzes a complete call demand and a key field in a text input by a user, and presets a template of a complete call report; the intelligent reasoning module constructs execution plans of other modules according to user exhaustion demands and report task characteristics, and formulates report task targets and output standards; the multi-source data scheduling module is in butt joint with a structured database of each administrative department related to an enterprise, and converts a natural language query requirement into an SQL statement capable of directly calling the database; the public opinion analysis module retrieves related news public opinions and performs emotional tendency analysis by using a large model; the intelligent integration module carries out integration according to a preset template of the exhaustion call report to give a comprehensive conclusion; the interactive generation module performs detail adjustment on the exhaustion report based on the real-time feedback of the user to obtain a final exhaustion report; the working efficiency is remarkably improved.

The invention discloses a multi-mode tactile and visual fusion identification method for medicine box braille salient point characters. The method comprises the following steps: acquiring three-dimensional morphology and pressure data of Braille salient points through a tactile sensor, acquiring an image and the like through a visual sensor, and performing preprocessing (improved U-Net segmentation, illumination correction and the like); hardware is utilized to trigger synchronization, an ICP algorithm is improved to complete space-time alignment, and a feature level (attention mechanism) and a decision level (D-S evidence theory) are fused; character semantics are analyzed by means of a multi-mode neural network, and multi-channel feedback is visualized through voice, tactile vibration and AR. A dynamic optimization mechanism is set, and sensor scheduling, incremental learning and safety verification are covered. The system comprises a tactile / visual perception module, a data processing module (FPGA + NPU), a feedback execution module and a communication module. According to the scheme, the problems of low traditional recognition precision and poor environmental adaptability are solved, high-precision medicine box braille recognition is achieved, and a reliable scheme suitable for multiple scenes is provided for safe medicine use of visually impaired people.

The invention provides an overseas public opinion security situation intelligent perception method and system fused with big data, and relates to the technical field of public opinion analysis, and the method comprises the steps: obtaining overseas public opinion data, extracting multi-dimensional features, employing a bidirectional cross attention mechanism to construct an adaptive weight matrix, generating a fusion feature matrix through multi-head time sequence convolution and a dynamic graph structure attention mechanism, constructing an evaluation index system, determining a weight based on information entropy and an evidence theory, determining an early warning level based on a multi-criterion decision, and generating management and control suggestions and early warning information.

The invention relates to the technical field of information retrieval, in particular to a vertical website deep crawling method based on intention recognition, which comprises the following steps: crawling data from a plurality of heterogeneous information sources, and constructing a dynamic knowledge graph; identifying a weak signal association path formed by connecting a plurality of relation edges in the dynamic knowledge graph, and converting the weak signal association path into a to-be-verified risk hypothesis; generating a data acquisition intention, and based on the to-be-verified risk hypothesis, generating a rejection query for searching reverse evidence as a first data acquisition intention; receiving an input query instruction, and performing semantic analysis on the query instruction to generate a second data acquisition intention; a multi-source crawling strategy is generated based on the type of the data collection intention, and the multi-source crawling strategy comprises a plurality of vertical website sources and crawling priorities of the vertical website sources; executing a deep crawling task according to the multi-source crawling strategy to obtain target data; and processing the target data, verifying the to-be-verified risk hypothesis, and judging whether the reverse evidence exists or not and judging the intensity of the reverse evidence.

The invention discloses an RAG large model functional gastrointestinal disease auxiliary diagnosis method, system and device based on a rule engine and a search engine, FASS vector library retrieval, Elasticsearch semantic retrieval and rule engine matching are combined, a gastrointestinal disease model obtained by performing fine adjustment based on a chatglm4-9b large model is combined, a threshold value is introduced to define a proportion, and an RAG large model functional gastrointestinal disease auxiliary diagnosis result is obtained. The gastrointestinal diseases are accurately diagnosed according to the medical case text information input by the user, the method is more intelligent, flexible and easy to understand, and the accuracy and efficiency of functional gastrointestinal disease diagnosis are greatly improved.

The invention relates to an enterprise knowledge base management system and method, which comprises portal display, business processing, basic configuration and business support which are mutually connected through standardized interfaces and processes to form a complete and efficient knowledge management system. The system can reduce the problem of knowledge dispersion, improve the retrieval efficiency and quickly position the required information.

The invention provides a government affair question-answering method and product based on a knowledge-enhanced large language model, and relates to the technical field of artificial intelligence. A current government affair question-answering system mainly adopts a manual reply mode and is low in efficiency, and a system based on RAG lacks a query and discrimination mechanism and cannot recognize intention differences and utilize dialogue contexts, so that the quality of retrieval knowledge is influenced. Aiming at the defects existing in the related technologies, the invention provides a government affair question answering method based on a knowledge enhancement large model, and particularly, aiming at the problem of low manual reply efficiency, a complete government affair hotline service framework containing intelligent question shunting and structured feedback is designed; aiming at the problem of poor knowledge retrieval quality of a traditional RAG system, a multi-stage semantic enhancement retrieval method is designed, cross-domain knowledge is supplemented through networking query, and accurate response and service closed loop of government affair consultation are realized.

A text processing method includes obtaining a query text, invoking a search engine interface based on the query text to obtain a plurality of text search results corresponding to the query text, obtaining, from the plurality of text search results, a plurality of answer text segments matching the query text, determining a relevance between the query text and each of the plurality of answer text segments, determining one of the plurality of answer text segments that corresponds to a maximum relevance as a reference text of the query text, and invoking a language model based on the query text and the reference text to obtain a reply text of the query text.

The invention discloses a false news detection method (MAFAN) based on multi-modal feature adaptive fusion, and the method comprises the steps: firstly, employing BERT to extract text features, employing ViT to extract image visual features, and designing a multi-branch feature extraction network based on wavelet feature enhanced convolution to extract the frequency domain information of an image; a double-stage fusion structure is adopted, firstly, text and frequency domain features are fused, and then the text and the frequency domain features are further fused with image features. Each stage is provided with a leading mode and an auxiliary mode to realize cross-mode interaction. A global gating mechanism is introduced to realize self-adaptive distribution of weights among modals, fusion weights are dynamically calculated according to semantic contribution of each modal feature, finally fusion features are self-adaptively generated through the gating mechanism, and dynamic adjustment of importance weights among the modals is realized. According to the mechanism, the adaptive capacity and the discrimination capacity of the model to multi-modal information are enhanced, and the accuracy and the robustness of false news detection are remarkably improved.

An RAG auxiliary generation type search engine method based on multiple rounds of expert and self-adaptive mixed retrieval comprises the steps that a crawler is used for crawling data, the crawled data are processed, a graph knowledge base is constructed through text partitioning and entity relation extraction, the graph knowledge base is coded into semantic vectors, and a vector database is established; by calculating similarity between subject vectors and literature vectors in a vector database, retrieving related literatures, utilizing a large language model to simulate a multi-expert role to generate differentiated viewpoints, and combining multiple rounds of dialogues and user feedback, generating a multi-view fused article to complete multiple rounds of expert answers; designing a double-layer retrieval mechanism according to different nodes and relationships in the retrieval atlas based on the atlas knowledge base; meanwhile, dense retrieval of a vector knowledge base of text coding is combined and utilized, a map-vector collaborative retrieval mechanism is designed, a self-adaptive mechanism and a session memory function are introduced, and multi-round question and answer response is carried out; according to the method and the system, higher logical contents can be generated.

The invention provides a service recommendation decision-making method based on large model retrieval enhancement generation and related equipment. The method comprises the following steps: acquiring an unstructured service demand; calling a large language model to perform semantic analysis and extension on the service demand, and generating an unstructured extended semantic text; encoding the extended semantic text to obtain a semantic query vector; querying a service description of each pre-stored service from a service database, respectively calculating a first similarity score between the semantic query vector and the service description, expanding a second similarity score between each keyword in the semantic text and the service description, and screening out a candidate service set having semantic association with the service demand; all corresponding application programming interfaces in the candidate service set are determined, and a final application programming interface set is screened out and recommended according to the use frequency of the various application programming interfaces, the semantic similarity among the labels and the semantic query vectors. According to the method and the device, the API recommendation accuracy is improved.

The present invention relates to the technical field of data retrieval. Disclosed are a data retrieval method and a network-attached storage device. The method comprises: acquiring data to be retrieved, wherein said data comprises at least one piece of sub-data to be retrieved, and each piece of said sub-data comprises one of text data, image data, and video data; on the basis of a feature extraction model, performing a feature extraction fusion operation on the preprocessed data to be retrieved to obtain a fusion feature vector corresponding to the data to be retrieved; on the basis of a target index structure corresponding to a vector database, determining a vector index corresponding to the fusion feature vector; and on the basis of a determined retrieval algorithm, performing a retrieval operation on the vector index in the vector database to obtain a retrieval result corresponding to the data to be retrieved. Therefore, by implementing the present invention, the data retrieval flexibility can be improved, so that more efficient retrieval services can be provided for users.

The invention relates to artificial intelligence security, in particular to a large model security alignment method based on intention reasoning and RAG guidance, which comprises the following steps: 1, performing structured enhancement on original input before large model reasoning, including security criterion introduction and prison break strategy matching; 2, on the basis of input enhancement, in combination with thinking chain reasoning and a multi-path sampling mechanism, fine-grained classification is carried out on user intentions, and illusion and misjudgment in the reasoning process are reduced; and 3, after the large model generates a response, performing multi-dimensional security assessment on the output by means of an external assessment tool, and guiding the large model to perform iterative optimization based on feedback to form a'generation-assessment-correction 'closed-loop mechanism, the method effectively resists prison break attacks, corrects the inherent defects of the large model, such as illusion, prejudice and the like, and improves the security of the large model. And by displaying the reasoning path and the improvement process, the interpretability of large model decision is enhanced, the general compliance constraint is broken through, and the precise compliance guarantee of high-sensitivity scenes such as medical treatment and finance is realized.

A method includes obtaining, as a response to a user query, a multitude of search results and corresponding similarity scores. The method further includes obtaining a multitude of data sources corresponding to the multitude of search results. The method further includes processing a multi-metric analytic function for the search results, and corresponding data sources to obtain a composite score for the search results. The method further includes selecting a subset of search results from the multitude of search results based on a composite score threshold to obtain a selected subset of search results. The method further includes selecting a corresponding subset of data sources from the multitude of data sources to obtain a selected subset of data sources. The method further includes generating a prompt to a large language model (LLM), including at least the user query, the selected subset of search results, and the selected subset of data sources.